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AIF-C01 practice question: Bias, variance and datasets (2 of 2)

Guidelines for Responsible AI A free question from our original AIF-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.

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A lender is collecting data to train a credit risk model that must treat applicants fairly. Past loan decisions were made by staff, and applicants come from many regions.

Which TWO dataset characteristics are MOST important for fair treatment? (Choose two.)

Choose two answers.

  1. A. Enough labeled applications from each applicant group the model will score, so that no group is underrepresented.
  2. B. Outcome labels reviewed for past bias, so that historical unfair refusals are not learned as the correct answer.
  3. C. The largest possible number of records, even if most of them come from a single region.
  4. D. Keeping protected attributes such as gender as predictors, since they show real differences in default risk.
  5. E. Only applications from the last two years, so that the model reflects current lending rules.
Show the answer and explanation

Answers: A, B

  • A. Right: Correct. A model learns little about groups with few examples, so representation across groups must be checked before its scores can be judged fair.
  • B. Right: Correct. If past decisions were unfair, using them as labels teaches the model to repeat that unfairness. The labels need review before training.
  • C. Wrong: Size does not fix bias. A very large dataset that mostly comes from one region still fails applicants elsewhere.
  • D. Wrong: Using protected attributes as predictors can lead to unfair treatment of protected groups, which the lender must avoid. Apparent differences in risk do not justify it.
  • E. Wrong: Recent records help the model stay current, but they do not address fairness across groups. Two years of biased decisions can still carry unfair patterns.

Explanation

Fair treatment depends on whether the training data represents every group the model will score, and on whether its labels reflect fair decisions. Data volume and recency matter less than these two points. This question tests identifying the dataset properties behind bias.

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